Sentence Similarity
sentence-transformers
Safetensors
Serbian
xlm-roberta
wsd
word-sense-disambiguation
serbian
knowledge-distillation
text-embeddings-inference
Instructions to use Tanor/serbian-wsd-distilled-e5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Tanor/serbian-wsd-distilled-e5-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Tanor/serbian-wsd-distilled-e5-large") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download runtime_verification.json from Tanor/serbian-wsd-distilled-e5-large: direct link, hf CLI and curl.
- Browser
- Download file 1.36 kB
-
https://huggingface.co/Tanor/serbian-wsd-distilled-e5-large/resolve/main/runtime_verification.json
- Command line
-
hf download hf://Tanor/serbian-wsd-distilled-e5-large/runtime_verification.json
-
curl -L -o runtime_verification.json https://huggingface.co/Tanor/serbian-wsd-distilled-e5-large/resolve/main/runtime_verification.json
1.36 kB
| { | |
| "repo_id": "Tanor/serbian-wsd-distilled-e5-large", | |
| "weight_sha256": "dc0cff1db0f5bf5072bf89b82b00b81cc30893d073914c8ce1d9b1f31a3b6b36", | |
| "device": "cpu", | |
| "local_files_only": true, | |
| "card_python_example_executed": true, | |
| "embeddings_shape": [ | |
| 3, | |
| 1024 | |
| ], | |
| "finite_values": true, | |
| "illustrative_scores": [ | |
| 0.1345173865556717, | |
| 0.42321670055389404 | |
| ], | |
| "illustrative_prediction": "ENG30-13152742-n", | |
| "accuracy_evaluation": false, | |
| "candidate_labels": [ | |
| "ENG30-06255777-n", | |
| "ENG30-13152742-n" | |
| ], | |
| "identifier_localization": { | |
| "mapping": { | |
| "paper": "list_papir", | |
| "leaf": "list_biljka" | |
| }, | |
| "original_prediction_label": "leaf", | |
| "model_inputs_and_numerical_outputs_unchanged": true, | |
| "inference_repeated": false, | |
| "date": "2026-09-27" | |
| }, | |
| "inventory_identifier_correction": { | |
| "mapping": { | |
| "list_papir": "ENG30-06255777-n", | |
| "list_biljka": "ENG30-13152742-n" | |
| }, | |
| "previous_prediction_label": "list_biljka", | |
| "model_inputs_and_numerical_outputs_unchanged": true, | |
| "inference_repeated": false, | |
| "definitions_are_illustrative_paraphrases": true, | |
| "candidate_inventory": "LexiSense-SR / Elexis-WSD-Repo.xlsx / SrWSD-v2", | |
| "inventory_rows_one_based": { | |
| "ENG30-06255777-n": 9915, | |
| "ENG30-13152742-n": 11790 | |
| }, | |
| "date": "2026-09-27" | |
| } | |
| } | |